Comparison between conventional and neural network classifiers for rat sleep-wake stage discrimination

This article describes an approach to selecting the most efficient classifier for rat sleep staging (waking, REM sleep and NREM sleep) discrimination. Three conventional (bayesian, linear, and euclidean) and two neural network (multilayer perceptrons integrating or not integrating contextual information) classifiers were compared. For each classifier, performances were presented in the form of a statistical concordance matrix comparing classifier results versus human expert results on 6 24-hour records (1 record per animal). Comparison between classifiers was based on the estimation and accuracy of global agreements. Interest was also focused on REM sleep state discrimination. The results show that neural network classifiers are appropriate tools to be integrated in an automatic rat sleep-wake stage system. The approach presented should help scientists in choosing a method of data classification.